Stochastic gene expression modeling with Hill function for switch-like gene responses.

Area of Science:

• Systems Biology
• Computational Biology
• Molecular Biology

Background:

• Gene expression mechanisms involve complex regulatory processes, including graded and switch-like responses.
• Existing stochastic models, like the Gillespie algorithm, often require gene cascades to simulate switch-like gene behavior.

Purpose of the Study:

• To develop a stochastic gene expression model that captures switch-like gene responses using Hill functions.
• To enhance the conventional Gillespie algorithm for simulating gene regulation with switch-like characteristics.

Main Methods:

• Proposed a modified Gillespie algorithm incorporating Hill functions into the simulation of eight gene expression processes.
• Estimated biologically relevant reaction rates based on existing scientific literature.
• Applied the model to the ScbA-ScbR system controlling antibiotic metabolite production.

Main Results:

• The modified Gillespie algorithm successfully described switch-like gene responses and oscillatory expressions.
• Observed that the Hill function effectively prevents protein activation below a concentration threshold, stabilizing the toggled switch model state.
• Model predictions were consistent with published experimental data for the ScbA-ScbR system.

Conclusions:

• The enhanced Gillespie algorithm with Hill functions provides a robust method for modeling switch-like gene expression.
• This approach accurately simulates gene regulatory networks exhibiting switch-like behaviors and oscillations.
• The model has implications for understanding and engineering biological systems, such as microbial metabolite production.